Trust on the world wide web: a survey
Foundations and Trends in Web Science
A recommendation system for browsing digital libraries
Proceedings of the 2009 ACM symposium on Applied Computing
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Trust and nuanced profile similarity in online social networks
ACM Transactions on the Web (TWEB)
Mixed collaborative and content-based filtering with user-contributed semantic features
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Social knowledge-based recommender system. Application to the movies domain
Expert Systems with Applications: An International Journal
Linked open data to support content-based recommender systems
Proceedings of the 8th International Conference on Semantic Systems
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In this paper we discuss the Recommendz recommender system. This domain-independent system combines the advantages of collaborative and content-based filtering in a novel way. By allowing users to provide feedback not only about an item as a whole, but also properties of an item that motivated their opinion, increased performance seems to be achieved. The features used to describe items are specified by the users of the system rather than predetermined using manual knowledge-engineering. We describe a method for combining descriptive features and simple ratings, and provide a performance analysis.